253 related articles for article (PubMed ID: 30523964)
1. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.
Zhao X; Li L; Lu W; Tan S
Phys Med Biol; 2018 Dec; 64(1):015011. PubMed ID: 30523964
[TBL] [Abstract][Full Text] [Related]
2. Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation.
Bi L; Fulham M; Li N; Liu Q; Song S; Dagan Feng D; Kim J
Comput Methods Programs Biomed; 2021 May; 203():106043. PubMed ID: 33744750
[TBL] [Abstract][Full Text] [Related]
3. Deep Learning for Variational Multimodality Tumor Segmentation in PET/CT.
Li L; Zhao X; Lu W; Tan S
Neurocomputing (Amst); 2020 Jun; 392():277-295. PubMed ID: 32773965
[TBL] [Abstract][Full Text] [Related]
4. Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network.
Guo Z; Guo N; Gong K; Zhong S; Li Q
Phys Med Biol; 2019 Oct; 64(20):205015. PubMed ID: 31514173
[TBL] [Abstract][Full Text] [Related]
5. Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.
Lei Y; Wang T; Jeong JJ; Janopaul-Naylor J; Kesarwala AH; Roper J; Tian S; Bradley JD; Liu T; Higgins K; Yang X
Med Phys; 2023 Jan; 50(1):274-283. PubMed ID: 36203393
[TBL] [Abstract][Full Text] [Related]
6. EFNet: evidence fusion network for tumor segmentation from PET-CT volumes.
Diao Z; Jiang H; Han XH; Yao YD; Shi T
Phys Med Biol; 2021 Oct; 66(20):. PubMed ID: 34555816
[TBL] [Abstract][Full Text] [Related]
7. SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.
Li GY; Chen J; Jang SI; Gong K; Li Q
Med Phys; 2024 Mar; 51(3):2096-2107. PubMed ID: 37776263
[TBL] [Abstract][Full Text] [Related]
8. MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention.
Wu Y; Jiang H; Pang W
Comput Biol Med; 2023 May; 158():106818. PubMed ID: 36966557
[TBL] [Abstract][Full Text] [Related]
9. MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images.
Wang F; Cheng C; Cao W; Wu Z; Wang H; Wei W; Yan Z; Liu Z
Comput Biol Med; 2023 Mar; 155():106657. PubMed ID: 36791551
[TBL] [Abstract][Full Text] [Related]
10. Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion.
Yuan C; Zhang M; Huang X; Xie W; Lin X; Zhao W; Li B; Qian D
Med Phys; 2021 Jul; 48(7):3665-3678. PubMed ID: 33735451
[TBL] [Abstract][Full Text] [Related]
11. Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation.
Fu X; Bi L; Kumar A; Fulham M; Kim J
IEEE J Biomed Health Inform; 2021 Sep; 25(9):3507-3516. PubMed ID: 33591922
[TBL] [Abstract][Full Text] [Related]
12. 3D FULLY CONVOLUTIONAL NETWORKS FOR CO-SEGMENTATION OF TUMORS ON PET-CT IMAGES.
Zhong Z; Kim Y; Zhou L; Plichta K; Allen B; Buatti J; Wu X
Proc IEEE Int Symp Biomed Imaging; 2018 Apr; 2018():228-231. PubMed ID: 31772717
[TBL] [Abstract][Full Text] [Related]
13. Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.
Zhao LM; Zhang H; Kim DD; Ghimire K; Hu R; Kargilis DC; Tang L; Meng S; Chen Q; Liao WH; Bai H; Jiao Z; Feng X
Phys Med Biol; 2023 Apr; 68(9):. PubMed ID: 37019119
[No Abstract] [Full Text] [Related]
14. Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss.
Xu Y; Klyuzhin I; Harsini S; Ortiz A; Zhang S; Bénard F; Dodhia R; Uribe CF; Rahmim A; Lavista Ferres J
Comput Biol Med; 2023 May; 158():106882. PubMed ID: 37037147
[TBL] [Abstract][Full Text] [Related]
15. Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images.
Huang Z; Guo Y; Zhang N; Huang X; Decazes P; Becker S; Ruan S
Comput Biol Med; 2022 Dec; 151(Pt A):106230. PubMed ID: 36306574
[TBL] [Abstract][Full Text] [Related]
16. Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net.
Matkovic LA; Wang T; Lei Y; Akin-Akintayo OO; Abiodun Ojo OA; Akintayo AA; Roper J; Bradley JD; Liu T; Schuster DM; Yang X
Phys Med Biol; 2021 Dec; 66(24):. PubMed ID: 34808603
[TBL] [Abstract][Full Text] [Related]
17. A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging.
Protonotarios NE; Katsamenis I; Sykiotis S; Dikaios N; Kastis GA; Chatziioannou SN; Metaxas M; Doulamis N; Doulamis A
Biomed Phys Eng Express; 2022 Feb; 8(2):. PubMed ID: 35144242
[TBL] [Abstract][Full Text] [Related]
18. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.
Zhou X
Adv Exp Med Biol; 2020; 1213():135-147. PubMed ID: 32030668
[TBL] [Abstract][Full Text] [Related]
19. DENSE-INception U-net for medical image segmentation.
Zhang Z; Wu C; Coleman S; Kerr D
Comput Methods Programs Biomed; 2020 Aug; 192():105395. PubMed ID: 32163817
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT.
Wang T; Xing H; Li Y; Wang S; Liu L; Li F; Jing H
BMC Med Imaging; 2022 May; 22(1):99. PubMed ID: 35614382
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
